English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we reveal that artificial neural network (ANN) assistedmultiple-input multiple-output (MIMO) signal detection can be modeled asANN-assisted lossy vector quantization (VQ), named MIMO-VQ, which is basicallya joint statistical channel quantization and signal quantization procedure. Itis found that the quantization loss increases linearly with the number oftransmit antennas, and thus MIMO-VQ scales poorly with the size of MIMO.Motivated by this finding, we propose a novel modular neural network basedapproach, termed MNNet, where the whole network is formed by a set ofpre-defined ANN modules. The key of ANN module design lies in the integrationof parallel interference cancellation in the MNNet, which linearly reduces theinterference (or equivalently the number of transmit-antennas) along thefeed-forward propagation; and so as the quantization loss. Our simulationresults show that the MNNet approach largely improves the deep-learningcapacity with near-optimal performance in various cases. Provided that MNNet iswell modularized, the learning procedure does not need to be applied on theentire network as a whole, but rather at the modular level. Due to this reason,MNNet has the advantage of much lower learning complexity than otherdeep-learning based MIMO detection approaches.

A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection